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4w4kt/waste-dataset-image

♻️ Waste Classification Image Dataset Dataset Summary This dataset contains standardized, multi-class waste images categorized across 9 target recycling categories. It was compiled, curated, and manually sanitized to eliminate noise, corrupted files, and class overlap in order to train high-performance convolutional neural networks and transfer learning backbones (such as EfficientNet-B5). The dataset contains a total of 28,840 labeled images, split into dedicated… See the full description on the dataset page: https://huggingface.co/datasets/4w4kt/waste-dataset-image.

sourceHugging Faceupdated 23d agoView on Hugging Face
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Dataset Card

♻️ Waste Classification Image Dataset

Dataset Summary

This dataset contains standardized, multi-class waste images categorized across 9 target recycling categories. It was compiled, curated, and manually sanitized to eliminate noise, corrupted files, and class overlap in order to train high-performance convolutional neural networks and transfer learning backbones (such as EfficientNet-B5).

The dataset contains a total of 28,840 labeled images, split into dedicated training and testing subsets, with an optimized Parquet download footprint of ~6.4 GB.

👥 Authorship & Curation

This dataset was assembled, verified, and manually cleaned through collaborative work by:

Manual Cleaning & Preprocessing

  • —Manual Sanitization: Images sourced across multiple raw collections were manually screened to eliminate duplicates, noisy/ambiguous items, and mislabeled classes.
  • —Resolution & Normalization: Input resolution normalization and adaptive resizing routines (including 456x456 and 528x528 dimensions matching EfficientNet compound scaling).
  • —Class Balancing: Class-weighted cross-entropy loss functions were designed alongside this distribution to mitigate sample imbalances across bins (such as battery and light bulb waste vs. clothes).

🌐 Sourced Datasets (Origins)

The base imagery originates from a synthesis of 7 open-source collections on Kaggle:

  1. 1.Custom Waste Classification Dataset - Wasif Mahmood
  2. 2.Garbage Dataset - Suman Kunwar
  3. 3.Waste Pictures - 且听风吟
  4. 4.Waste Classification Dataset - Kaan Çerkez
  5. 5.Diverse Tools Image Dataset for Machine Learning - Oort Datahub
  6. 6.Garbage Dataset from Various Sources - Butleriii
  7. 7.Object Detection: Batteries, Dice, and Toy Cars - Márk Antal Csizmadia

📊 Dataset Structure

Partitions & Size

SplitNumber of ImagesDownload Size
train23,264~5.8 GB
test5,576~0.6 GB
Total28,840~6.4 GB

Classes & Indices

IDClass LabelCategory Focus
0E-wasteSmall appliances, circuit boards, components
1battery wasteAlkaline, lithium, cylindrical, and car batteries
2clothesTextiles, garments, fabrics
3glass wasteBottles, jars, broken glass fragments
4light bulbsIncandescent, LED, fluorescent tubes
5metal wasteAluminum cans, scrap metal, tins
6organic wasteFood leftovers, peels, biodegradable waste
7paper wasteCardboard boxes, paper sheets, packaging
8plastic wastePET bottles, containers, bags

💻 Usage

Load the data directly through the Hugging Face datasets library:

python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("4w4kt/waste-dataset-image")

# Inspect a train sample
sample = dataset["train"][0]
image = sample["image"]
label_id = sample["label"]
label_name = dataset["train"].features["label"].int2str(label_id)

print(f"Sample Class: {label_name} (ID: {label_id})")

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